Regulating top albedo and bottom emissivity of concrete roof tiles for reducing building heat gains

Qin, Yinghong; He, Yuhui; Wu, Bo; Ma, Shangjun; Zhang, Xingui

doi:10.1016/j.enbuild.2017.09.090

Cited by 103 publications

(26 citation statements)

References 28 publications

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“…Distillation columns are categorized into two groups of packed-bed and tray-based on the angle amongst liquid and gas [10]. In pack-type columns, commonly two kinds of packing, namely, structure and random packings, are applied [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Computational Fluid Dynamics Characterization of High‐Capacity Structured Packing

et al. 2020

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The novel wire gauze structured packing, PACK‐860, was characterized by means of numerical methods. The main features of PACK‐860 such as height equivalent to a theoretical plate (HETP) and dry/wet pressure drop were evaluated. The flow structure in this packing was described by numerical simulation. To estimate the amount of HETP and dry/wet pressure drop, three‐dimensional computational fluid dynamics (3D CFD) modeling with the Eulerian‐Eulerian multiphase approach was applied. The average relative errors between the results obtained from CFD simulation and experimental findings for mass transfer efficiency and wet and dry pressure drop were assessed. Numerical observations were found to agree well with the empirical results, proving the reliability of CFD simulations for modeling separation processes.

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Section: Introductionmentioning

confidence: 99%

Computational Fluid Dynamics Characterization of High‐Capacity Structured Packing

et al. 2020

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“…Furthermore, in order to get insight into the models’ confidence, the most commonly used indices, including root mean square error (RMSE), coefficient of determination ( R 2 ), mean absolute error (MAE), relative root mean square error (RRMSE), and mean absolute percentage error (MAPE), were used, respectively, as:

RMSE = {(}{, (][, \sum_{1}^{n} {false(O_{i} - P_{i} false)}^{2}) false/ n)}^{1 / 2},

R^{2} = 1 - \frac{{normalR normalM normalS normalE}^{2}}{σ^{2}},

M A E = \frac{1}{n} \sum_{1}^{n} false| O_{i} - P_{i} false|,

R R M S E = \frac{{(}{[\sum_{1}^{n} {()(O_{i} - P_{i})}^{2}] / n)}^{1 (/, 2)}}{\frac{1}{n} \sum_{1}^{n} false| O_{i} - P_{i} false|} \times 100,

M A P E = \frac{1}{n} \sum_{i = 1}^{n} false| \frac{P_{i} - O_{i}}{O_{i}} false| \times 100 .

…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, in order to get insight into the models' confidence, [41][42][43] the most commonly used indices, including root mean square error (RMSE), coefficient of determination (R 2 ), 44 mean absolute error (MAE), relative root mean square error (RRMSE), and mean absolute percentage error (MAPE), were used, respectively, as:…”

Section: Support Vector Regressionmentioning

confidence: 99%

Technological assessment and modeling of energy‐related CO₂ emissions for the G8 countries by using hybrid IWO algorithm based on SVM

Ghazvini

Madvar

Ahmadi

et al. 2020

Energy Science & Engineering

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Recently, energy‐related CO2 emissions are considered as one of the most crucial issues and are promptly augmented due to further urbanization. In this paper, in order to model and calculate yearly CO2 emission, an artificial neural network is used. For the first time, the IWO‐SVM method has been applied in modeling energy‐related CO2 emissions. In this regard, consumption of different energy sources such as renewable energy, natural gas, coal, and oil, and GDP of the G8 countries in various years (from 1990 to 2016) are regarded as input in the present study. For the aim of evaluating the exact ability of the SVM and SVM‐IWO models, the performance of these models in three different modes is carried out on the basis of the number of data in the test and train sections. For this purpose, implementations are split into three categories (a = 80% of the data for the train section and 20% for the test section; b = 70% of the data for the train section and 30% for the test section; and c = 60% of the data for the train section and 40% for the test section). Furthermore, five scenarios were selected on the basis of the number of input parameters and input parameters for achieving the best model. As indicated in the results in all scenarios, the correlation of the model with the hybrid invasive weed algorithm based on SVM is more favorable than that in the support vector machine model, due to better training of the SVM‐IWO model than the SVM model. Moreover, the technological orientations of the G8 countries to mitigate CO2 emissions are determined through patent analysis. While the patents have essential information, investigating the published patent by a country could be helpful for determination of technological orientations. Hence, all published patents by these countries are extracted and deeply analyzed. In the next step, to find out main technological approaches, all patents and their intents have been studied. Eventually, the technological life cycles and trends of each main technology are drawn.

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“…Indeed, the connection of terms could considerably help us to find optimum solutions for scientific and engineering problems. Meanwhile, the efficiency of this method is high and scientists have to look this technique as a reliable and robust method …”

Section: Application Of Gmdh‐type Nn For Modeling Of the Problemmentioning

confidence: 99%

Application of neural network on heat transfer enhancement of magnetohydrodynamic nanofluid

Gerdroodbary

2019

Heat Trans. Asian Res.

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Regulating top albedo and bottom emissivity of concrete roof tiles for reducing building heat gains

Cited by 103 publications

References 28 publications

Computational Fluid Dynamics Characterization of High‐Capacity Structured Packing

Computational Fluid Dynamics Characterization of High‐Capacity Structured Packing

Technological assessment and modeling of energy‐related CO₂ emissions for the G8 countries by using hybrid IWO algorithm based on SVM

Application of neural network on heat transfer enhancement of magnetohydrodynamic nanofluid

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Regulating top albedo and bottom emissivity of concrete roof tiles for reducing building heat gains

Cited by 103 publications

References 28 publications

Computational Fluid Dynamics Characterization of High‐Capacity Structured Packing

Computational Fluid Dynamics Characterization of High‐Capacity Structured Packing

Technological assessment and modeling of energy‐related CO2 emissions for the G8 countries by using hybrid IWO algorithm based on SVM

Application of neural network on heat transfer enhancement of magnetohydrodynamic nanofluid

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Product

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Technological assessment and modeling of energy‐related CO₂ emissions for the G8 countries by using hybrid IWO algorithm based on SVM